docs: def _check_fit_data(self, X):
          """Verify that the number of samples given is larger than k"""
          X = check_array(X, accept_sparse="csr", dtype=[np.float64, np.float32])
          n_samples, n_features = X.shape
          if X.shape[0] < self.n_clusters:
              raise ValueError(
                  "n_samples=%d should be >= n_clusters=%d"
                  % (X.shape[0], self.n_clusters)
              )
  
          for ee in range(n_samples):
              if sp.issparse(X):
                  n = sp.linalg.norm(X[ee, :])
              else:
                  n = np.linalg.norm(X[ee, :])
  
              if np.abs(n - 1.) > 1e-4:
                  raise ValueError("Data l2-norm must be 1, found {}".format(n))
  
          return X